Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the user's buying behavior or preference history, generated from the user's interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumer's buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits—Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem—that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.

对用户个人资料进行建模是设计个性化推荐的重要因素之一。在计算机科学中对用户资料进行建模的传统方法是收集和概括用户的购买行为或偏好历史记录，这些行为是由用户与推荐系统的交互产生的。然而，根据消费者行为研究，诸如性格特征之类的内部因素会影响消费者的购买行为。现有研究试图使五大个性特征适应个性化建议。但是，尽管研究表明这些特征在某种程度上可以用于个性化推荐，但是五种人格特征与实际消费者的购买行为之间的因果关系尚未得到验证。在本文中，我们提出了一种新颖的方法来预测与购买行为相关的四个人格特征：外向性，公众自我意识，对独特性的渴望和自尊。所提出的方法通过分析社交网络服务上的用户行为，自动为每个用户构建用户个性特征预测模型。对收集到的Facebook数据进行分析的实验结果表明，与使用先前研究中提出的变量的方法相比，所提出的方法可以更精确地预测用户个性特征。所提出的方法通过分析社交网络服务上的用户行为，自动为每个用户构建用户个性特征预测模型。对收集到的Facebook数据进行分析的实验结果表明，与使用先前研究中提出的变量的方法相比，所提出的方法可以更精确地预测用户个性特征。所提出的方法通过分析社交网络服务上的用户行为，自动为每个用户构建用户个性特征预测模型。对收集到的Facebook数据进行分析的实验结果表明，与使用先前研究中提出的变量的方法相比，所提出的方法可以更精确地预测用户个性特征。